图 书 馆
使用图表汇总链接预测注释图表数据集Link prediction for annotation graph datasets using graph summarization |
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| 课程网址: | http://videolectures.net/iswc2011_thor_graph/ |
| 主讲教师: | Andreas Thor |
| 开课单位: | 马里兰大学 |
| 开课时间: | 2011-11-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Annotation graph datasets are a natural representation of scientifi c knowledge. They are common in the life sciences where genes or proteins are annotated with controlled vocabulary terms (CV terms) from ontologies. The W3C Linking Open Data (LOD) initiative and semantic Web technologies are playing a leading role in making such datasets widely available. Scientists can mine these datasets to discover patterns of annotation. While ontology alignment and integration across datasets has been explored in the context of the semantic Web, there is no current approach to mine such patterns in annotation graph datasets. In this paper, we propose a novel approach for link prediction; it is a preliminary task when discovering more complex patterns. Our prediction is based on a complementary methodology of graph summarization (GS) and dense subgraphs (DSG). GS can exploit and summarize knowledge captured within the ontologies and in the annotation patterns. DSG uses the ontology structure, in particular the distance between CV terms, to filter the graph, and to find promising subgraphs. We develop a scoring function based on multiple heuristics to rank the predictions. We perform an extensive evaluation on Arabidopsis thaliana genes. |
| 关 键 词: | 注释图数据集; 基因蛋白; 语义Web |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-05：lxf |
| 阅读次数: | 54 |